FYI – This is going to be a blog series, and this blog would be the first of the series. It will solely focus on the different types of data analytics.

Data is oil, while raw data is like crude oil. Today, organizations can easily get hold of data but what after that? They need to comprehend that data to derive meaningful insights to survive in this unpredictable and competitive environment.

Fortunately, we’ve data analytics, but you also have to understand these 7 different types of analytics to get the most out of the former – scroll below:

Descriptive Analytics

It examines the raw data and summarizes what happens in an enterprise. For that, it focuses on two questions:

What happened?

What is happening?

Prescriptive Analytics

Lauded for answering only the precise questions, this type of analytics comes with a laser-like approach. It is largely used in the healthcare industry to keep a check on the patient population by figuring out the numbers of clinically obese patients.

Predictive Analytics

Use of data + Machine Learning + Advanced Algorithms sums up predictive analytics – it helps us to go beyond what just happened now and assess the future.

Causal Analytics

It functions by relying on a number of random variables and predicts what is likely to happen next. It aids analytics predict even if one element of the variable remains changed. It works great when you are dealing with humongous volumes of data.

Diagnostic Analytics

We seek diagnostic analytics when we want to ascertain why this happened. To best explain, let’s take a social media marketing campaign – with this analytics, you can access the number of likes, comments, mentions, reviews and followers. It helps boost your campaign.

Exploratory Analytics

It best determines general patterns from the raw data and identifies outliers. This approach is one of the most anticipated as compared to other types of analytics. However, before putting it to use, make sure you know well when does outliers occur and how other environmental variables help in making informed decisions.

Mechanistic Analytics

This branch of analytics is a combination of equations of engineering and physical sciences. With this tool, the data scientists can easily decipher identifiable alterations and determine parameters provided they already know the equation.

Inferential Analytics

Take a tiny fragment of information from the population and use it as a base to derive parameters about a larger population – this is what inferential analytics is all about. It helps in evaluating specific facets of a large population.

Needless to say, data analytics is about to change the way we live, work and do business with. Though it has already become an order of the day, a majority of organizations are yet to become completely data-driven. The world needs a large pool of data scientists and data analysts – find out the best data analytics courses in Kolkata and transform your career for good!